Polychronis Kolokoussis

Investigation of hyperspectral remote sensing for mapping asphalt road conditions

An investigation of hyperspectral remote sensing for mapping asphalt road conditions is undertaken in this study. Hyperspectral data acquired by the GER1500 radiometer and the Compact Airborne Spectrographic Imager (CASI) 550 sensor have been analysed, processed and interpreted. Field radiometer data were used to provide high-quality spectral measurements for developing a spectral library for asphalt, defining potential categories of the asphalt condition and minimizing the dimension of the hyperspectral space. Analysis of spectral signatures indicated that asphalt condition is affected by asphalt age, material quality and road circulation, and that it led to the definition of five potential categories. Two of them indicate asphalt in high distress and surfaces that need rehabilitation. Among several others, the following processing methods were revealed as the most suitable for detecting asphalt condition: Principal Component Analysis (PCA), thresholding of colour transformation images, unsupervised classification Iterative Self-organizing Data Analysis (IsoData), supervised classification Spectral Angle Mapper (SAM) and texture measurements using the Grey-level Co-occurrence Matrix operator. The results indicated that hyperspectral remote sensing is capable of mapping asphalt road conditions with respect to the categorization proposed within this study.

Integrating thermal and hyperspectral remote sensing for the detection of coastal springs and submarine groundwater discharges

This research focuses on the investigation of remote-sensing techniques for the detection of coastal sub-aerial springs and submarine groundwater discharges using airborne thermal and hyperspectral imagery. Very high spatial resolution thermal and hyperspectral images were acquired using Thermal Airborne Broadband Imager 320 (TABI-320) and Compact Airborne Spectrographic Imager 550 (CASI-550) sensors. Extensive in situ spectroradiometer and oceanographic measurements were carried out in parallel with thermal and hyperspectral image acquisitions. Experiments and analysis of the data show that the combined use of very high spatial resolution airborne thermal and hyperspectral sensors for the detection of relatively small sub-aerial coastal springs and submarine groundwater discharges proves to be a very efficient and operational method. Very high spatial resolution thermal data were able to detect even very small coastal sub-aerial springs. On the other hand, the hyperspectral data were the most appropriate for detecting relatively small submarine groundwater discharges, which were not detected on thermal imagery, due to the increase in turbidity that these discharges cause. This is confirmed by the strong correlations between the hyperspectral data and the in situ measured turbidity-related water inherent optical properties.

De-shadowing of airborne imagery using at-sensor downwelling irradiance data

Cast shadows caused by sparse clouds usually degrade spaceborne and airborne imagery. They result from the decrease of the direct solar beam due to the presence of a non-transparent cloud. The reduction of the downwelling solar flux density can be quantified during an air campaign, if the aircraft flies beneath the cloud and is equipped with an add-on instrument that measures the total downwelling solar irradiance. The objective of this work is to exploit such data for the de-shadowing of airborne hyperspectral imagery. Initially, the specific illumination and viewing conditions during the image acquisition, which allow the use of at-sensor downwelling irradiance data for the de-shadowing of airborne hyperspectral imagery, are outlined. Then a methodology is proposed that estimates the radiometric enhancement coefficients from the at-sensor irradiance data and correlates them with the image data using a shadow map. Improvements of the quality of the shadow maps are suggested. Performance assessment showed that at-sensor irradiance data could be satisfactorily utilized for compensating the cast shadow effects on remotely sensed imagery. It also highlighted the importance of generating and using an accurate shadow map and the particular difficulties for the air campaign planning raised by the requirement of exploitable at-sensor irradiance data.

Effects of band selection on endmember extraction for forestry applications

In spectral unmixing theory, data reduction techniques play an important role as hyperspectral imagery contains an immense amount of data, posing many challenging problems such as data storage, computational efficiency, and the so called “curse of dimensionality”. Feature extraction and feature selection are the two main approaches for dimensionality reduction. Feature extraction techniques are used for reducing the dimensionality of the hyperspectral data by applying transforms on hyperspectral data. Feature selection techniques retain the physical meaning of the data by selecting a set of bands from the input hyperspectral dataset, which mainly contain the information needed for spectral unmixing. Although feature selection techniques are well-known for their dimensionality reduction potentials they are rarely used in the unmixing process. The majority of the existing state-of-the-art dimensionality reduction methods set criteria to the spectral information, which is derived by the whole wavelength, in order to define the optimum spectral subspace. These criteria are not associated with any particular application but with the data statistics, such as correlation and entropy values. However, each application is associated with specific land c over materials, whose spectral characteristics present variations in specific wavelengths. In forestry for example, many applications focus on tree leaves, in which specific pigments such as chlorophyll, xanthophyll, etc. determine the wavelengths where tree species, diseases, etc., can be detected. For such applications, when the unmixing process is applied, the tree species, diseases, etc., are considered as the endmembers of interest. This paper focuses on investigating the effects of band selection on the endmember extraction by exploiting the information of the vegetation absorbance spectral zones. More precisely, it is explored whether endmember extraction can be optimized when specific sets of initial bands related to leaf spectral characteristics are selected. Experiments comprise application of well-known signal subspace estimation and endmember extraction methods on a hyperspectral imagery that presents a forest area. Evaluation of the extracted endmembers showed that more forest species can be extracted as endmembers using selected bands.

A methodology for monitoring the upwelling phenomenon using Sentinel-3 products

Upwelling is a phenomenon which involves wind-driven motion of dense, cool, and usually nutrient-rich deep water towards the ocean surface replacing the warmer usually nutrient-depleted surface water. The deeper water is rich in nutrients, favoring the growth of seaweed and phytoplankton, and is characterized by high Chlorophyll-a (Chl-a) concentrations. Upwelling regions are considered as the most fertile fishing grounds and a so fundamental economic resource. In this paper, an approach for satellite monitoring of coastal upwelling regions is proposed based on Sea Surface Temperature (SST), and Chl-a information from Sentinel-3 OLCI Level-2 products, as well as, wind information from Copernicus Marine Environment Monitoring Service global product. The approach consists of the following parts. Firstly, using wind information the time periods of upwelling-favorable wind were identified. For these time periods, a thermal map is produced from Sentinel-3 SST products using a clustering approach. From the clustering result a vector file which contains the cold patches of upwelled water is generated. Lastly, Chl-a concentration information is parsed in the vector file. The approach was tested over the Benguela Upwelling System. The results are satisfactory and the proposed methodology is capable of detecting and monitoring the upwelling spatial extent and variations, as well as Chl-a concentration changes in the upwelling regions. The proposed methodology will be utilized within the framework of SEO-DWARF H2020 programme (MSCA-RISE-691071), in order to create the relevant metadata for Sentinel-3 OLCI Level-2 products.

Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece

Abstract The aim of this study is to investigate the potential of Sentinel-2 imagery for the identification and determination of forest patches of particular interest, with respect to ecosystem integrity and biodiversity and to produce a relevant biodiversity map, based on Simpson’s diversity index in Taxiarchis university research forest, Chalkidiki, North Greece. The research is based on OBIA being developed on to bi-temporal summer and winter Sentinel-2 imagery. Fuzzy rules, which are based on topographic factors, such as terrain elevation and slope for the distribution of each tree species, derived from expert knowledge and field observations, were used to improve the accuracy of tree species classification. Finally, Simpson’s diversity index for forest tree species, was calculated and mapped, constituting a relative indicator for biodiversity for forest ecosystem organisms (fungi, insects, birds, reptiles, mammals) and carrying implications for the identification of patches prone to disturbance or that should be prioritized for conservation.

Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach

This paper proposes an efficient methodology for combining multiple remotely sensed imagery, in order to increase the classification accuracy in complex forest species mapping tasks. The proposed scheme follows a decision fusion approach, whereby each image is first classified separately by means of a pixel-wise Fuzzy-Output Support Vector Machine (FO-SVM) classifier. Subsequently, the multiple results are fused according to the so-called multiple spectral– spatial classifier using the minimum spanning forest (MSSC-MSF) approach, which constitutes an effective post-regularization procedure for enhancing the result of a single pixel-based classification. For this purpose, the original MSSC-MSF has been extended in order to handle multiple classifications. In particular, the fuzzy outputs of the pixel-based classifiers are stacked and used to grow the MSF, whereas the markers are also determined considering both classifications. The proposed methodology has been tested on a challenging forest species mapping task in northern Greece, considering a multispectral (GeoEye) and a hyper-spectral (CASI) image. The pixel-wise classifications resulted in overall accuracies (OA) of 68.71% for the GeoEye and 77.95% for the CASI images, respectively. Both of them are characterized by high levels of speckle noise. Applying the proposed multi-source MSSC-MSF fusion, the OA climbs to 90.86%, which is attributed both to the ability of MSSC-MSF to tackle the salt-and-pepper effect, as well as the fact that the fusion approach exploits the relative advantages of both information sources.

Development of a New Automatic Relative Radiometric Normalization Methodology for Multispectral and Hyperspectral Images

The concept of a relative radiometric normalization (RRN) for multitemporal multispectral and hyperspectral images is investigated. A new automatic RRN methodology is presented: the Normalized Proximity and Similarity Methodology (NPSM). The NPS methodology introduces two spectral metrics, the absolute normalized difference (AND) and the absolute normalized ratio difference (ANRD), to select Invariant Pixels (IPs). A new metric is also introduced for the evaluation of the proposed methodology: the root mean square error (RMSE) between the values of the initial subject image and the relevant reconstructed subject image. The NPSM is validated using 9 ASTER and 14 Hyperion images. Results demonstrate the high performance of the methodology on both hyperspectral and multispectral data.